What Is Everything Claude Code (ECC)? Skills, Agents, Hooks, Memory and Security Explained
What Is Everything Claude Code (ECC)? Skills, Agents, Hooks, Memory and Security Explained
Quick Answer
Everything Claude Code (ECC) is a community-built, open-source layer for AI coding agents, created by affaan-m and hosted at github.com/affaan-m/ECC. It packages skills, agents, rules, hooks, memory and security scanning into a reusable system that runs natively on Claude Code and adapts to other harnesses such as OpenAI Codex. ECC is not official Anthropic software, and it does not automatically run an engineering workflow for you — it provides the building blocks you assemble into one.
ECC at a Glance
Snapshot: September 2026. ECC iterates quickly, so treat the repository as the source of truth for anything version-specific.
- Repository: github.com/affaan-m/ECC
- Maintainer: affaan-m (a community project, not official Anthropic software)
- Positioning: reusable AI coding harness optimization system — a packaged layer of skills, agents, rules, hooks, memory and security scanning, rather than a single prompt or skills pack
- Primary / native harness: Claude Code
- Other harnesses: OpenAI Codex is the most developed secondary target; OpenCode, Cursor, Gemini and Zed are referenced as additional targets with less complete, faster-changing adaptation (details below)
- Security component: AgentShield, which ships inside ECC
- Recommended install entry: the repository README and docs — the recommended method has changed before and will change again, so follow the current README
- Current release and component counts: intentionally not pinned here. Skill, agent, command and hook counts, as well as release numbers, change frequently; check the repository’s README and releases for current figures.
What Is Everything Claude Code (ECC)?
Everything Claude Code is a reusable AI coding harness optimization system. In plain terms: instead of copying the same instructions, conventions, review prompts and security checks into every project and every AI coding tool, ECC packages them into components that you can select, combine and carry between projects.
┌───────────────────────────────────────────────────────────────┐│ Everything Claude Code (ECC) — reusable layer │├───────────┬───────────┬───────────┬───────────┬───────────────┤│ Skills │ Agents │ Rules │ Hooks │ Memory │├───────────┴───────────┴───────────┴───────────┴───────────────┤│ Instincts / continuous learning AgentShield / security │├───────────────────────────────────────────────────────────────┤│ components you assemble into a workflow per harness │└───────────────────────────────────────────────────────────────┘The honest way to read that diagram: ECC gives you components and conventions for wiring them together. It does not install a fixed, self-running pipeline on top of every tool.
Why the name “Everything Claude Code” is misleading
“Claude Code” is in the name because the project started around Claude Code, and Claude Code is still its native environment. Two things about the name confuse people:
- It overstates the Claude-only scope — ECC targets more harnesses than Claude Code.
- “Everything” reads like a promise of completeness or automation. ECC is not “everything your agent will ever need, automatically.” It is a curated, opinionated set of building blocks that you still have to choose, configure and maintain.
ECC vs a skills pack
A skills pack is, at its core, a folder of reusable instructions the agent can load. ECC includes skills, but it also includes agents, rules, hooks, memory conventions and security scanning — plus the glue that makes them cooperate (for example, hooks that trigger checks and rules that load memory at session start). In short: a skills pack adds capabilities; ECC tries to add structure and workflow. That broader scope is also why ECC is more complex than a skills pack, a tradeoff discussed later in this post.
Why ECC Exists
Modern AI coding tools are powerful but fragmented. Anyone who uses them seriously ends up rebuilding the same things over and over:
- the same coding rules and conventions for every project
- the same review and testing instructions
- the same security checks
- the same conventions for helping the agent remember decisions
- the same agent definitions and tool configuration
And all of that has to be redone when switching between tools such as Claude Code, Codex, OpenCode or Cursor. Good prompts alone do not guarantee consistent engineering quality, and one CLAUDE.md file does not travel with you. ECC exists to turn that repeated setup into shared, reusable infrastructure instead of per-project copy-paste.
This is also why ECC has drawn attention on GitHub, X and Reddit over the past year: it arrived at a moment when many people were hitting exactly this fragmentation, and it grew from a Claude Code configuration into a broader agent-harness infrastructure project. That trajectory is part of the context, but it is not a quality claim — popularity is not the same as correctness, and the sections below focus on what ECC actually does.
How ECC Is Structured
ECC is organized around a small set of building blocks. To avoid overstating what each one does, each section below follows three questions:
- What it is — the component, concretely.
- How it is actually used — the file, command, skill, hook or workflow that triggers it.
- What it does NOT guarantee — the boundary, so you are not surprised later.
Skills
What it is. Packaged, task-focused capabilities the agent can load on demand — for example, “write a Django test” or “review a pull request”. Instead of pasting the same long instructions into every chat, you point the agent at a skill, or the agent recognizes that a skill applies.
How it is actually used. Skills live in the ECC setup as files the agent can read when a task matches. You invoke them by asking, or the agent selects one based on its instructions. They are loaded into context when used, not all at once.
What it does NOT guarantee. ECC does not automatically load or run every installed skill on every task. Skills are not self-improving: a skill gets better only when someone edits it. And skill behavior is not identical across harnesses — see the harness section.
Agents
What it is. Specialized roles with their own instructions and tool access — a reviewer, a tester, a debugger. This mirrors how Claude Code models subagents: a definition with a description, a prompt and an allowed tool set.
How it is actually used. You (or the main agent, when your instructions allow delegation) invoke an agent for a specific job, such as “review this diff”. In Claude Code this maps to native subagents; in other harnesses the mechanism differs.
What it does NOT guarantee. Agents do not run continuously or review every change on their own. They act when invoked, and their quality is bounded by how well they are defined. A “review agent” improves review consistency, but it does not guarantee that every bug is caught.
Rules
What it is. Coding conventions and workflow constraints — similar in spirit to a CLAUDE.md file that is loaded at the start of a session, but shared across projects rather than maintained per project.
How it is actually used. Rules are loaded as context when a session starts (or when the agent is instructed to consult them). ECC maps them onto whatever convention file the harness supports — CLAUDE.md-style for Claude Code, AGENTS.md-style for others.
What it does NOT guarantee. Rules describe expected behavior; they are not enforced at runtime. An agent can still violate a rule, and overlapping rules from different sources can contradict each other and produce unpredictable behavior.
Hooks
What it is. Event handlers that run before or after agent events such as tool use or a user prompt — a native Claude Code mechanism that ECC configures rather than reinvents.
How it is actually used. ECC ships hook configurations that run checks at defined points. For example, a hook can inspect a command before it executes and deny it when it looks dangerous, or run a test after an edit.
What it does NOT guarantee. Hooks only fire where the harness supports them and only for the events the harness exposes. They do not exist identically in every tool, and a hook that blocks one command shape can miss a slightly different one. Hooks add real value, but they are not a security boundary by themselves.
Memory
What it is. Persistent context: architecture decisions, naming conventions, “we chose Postgres, not MySQL”. Without it, each new session starts from zero and re-litigates decisions already made.
How it is actually used. In ECC, memory is best understood as written context, not a hidden database. Decisions and conventions are stored in files you control (project-level or shared), and rules or instructions load them at the start of a session. Writing to memory is an explicit step: the agent (or you) records a decision as part of the workflow, and later sessions read it. Whether memory is global or project-level, and how it is loaded, is configuration you choose; the exact implementation details have changed between versions.
What it does NOT guarantee. Memory does not silently capture everything from every session, and ECC does not learn your preferences by observing you. If nothing is written and nothing is loaded, there is no memory. It is a workflow convention plus supporting files, not automatic recall.
Instincts / Continuous Learning
What it is. The part of ECC that moves beyond a static pack: recurring patterns from real sessions get captured and promoted into reusable rules or skills, so the setup reflects how you actually work instead of staying frozen at what you wrote on day one.
How it is actually used. In practice this is an explicit workflow, not a background process. After a session you (or an agent following an explicit “improve” step) identify a pattern that worked, capture it as a rule or skill, and review it before it is added. The promotion step is deliberate.
What it does NOT guarantee. ECC does not automatically get smarter as you use it. There is no silent retraining and no autonomous self-modification: patterns are promoted when someone runs the capture step and decides to keep them. If continuous learning sounds automatic, treat that as a sign to check the current documentation, because this is one of the most over-hyped descriptions in AI coding tooling.
AgentShield / Security
What it is. AgentShield is the security component that ships inside ECC. It is best understood as a configuration and file scanner rather than a real-time firewall that pauses a live agent: it reviews the agent-facing content ECC manages — skills, rules, hooks, commands and similar configuration — and flags dangerous or risky content such as destructive commands, suspicious tool instructions or exposed secrets. Some of its checks can also be wired into harness hooks so they run at defined points.
How it is actually used. AgentShield is part of the ECC setup; you enable it when you adopt ECC, and its scans run as part of the configured checks. Where blocking happens, it works through the harness’s own hook mechanism — the only interception point a layer like ECC has in Claude Code or similar tools.
What it does NOT guarantee. AgentShield does not observe or block arbitrary commands that run outside the harness’s hook events, and it is not a sandbox or a kernel-level guard. It cannot catch every prompt-injection or every bad skill, especially if the offending file is not covered by the current scan set. The precise set of checks changes as the project iterates, so check the repository for the current scope before you rely on it as your only security control.
How an ECC-Assisted Workflow Works
The real value of ECC is not any single component. It is the workflow those components feed: plan → test → implement → review → verify → remember → improve.
A key distinction: ECC gives developers reusable building blocks for constructing this workflow. It does not automatically run this workflow. What follows is one possible ECC-assisted workflow assembled from those blocks, not a fixed state machine that activates on install.
A possible ECC-assisted workflow could look like this. Imagine asking an agent to add login authentication to a small existing API:
- Plan — the agent reads the project rules and memory, then writes a short plan before touching code. [Native ECC component — rules and memory are loaded context; the planning step is a convention you instruct.]
- Test — you invoke a testing skill, which writes a failing test for the login flow first. [User-invoked — the skill exists, but someone has to call it or instruct the agent to use it.]
- Implement — the agent implements the feature, guided by coding rules instead of guessing. [Native ECC component + harness behavior.]
- Review — you delegate to a review agent to check the diff for common mistakes. [User-invoked — ECC provides the role; the agent does not review itself unless told to.]
- Verify — configured hooks run checks, and the test suite runs again. [Harness-dependent — hooks fire only where the harness supports them.]
- Remember — as part of the workflow, the agent records the auth decision in memory so the next session does not redo the analysis. [Explicit step — this only happens if the workflow tells the agent to write it down.]
- Improve — if a recurring pattern surfaced, you capture it as a rule or skill for next time. [Recommended practice — manual promotion, not automatic learning.]
plan → test → implement → review → verify → remember → improve │ │ │ │ │ │ │ └──────┴────────┴───────────┴────────┴─────────┴─────────┘ each step is assembled from ECC components by the userWithout ECC, steps 1, 4, 5 and 6 depend on whatever you manually type into each session. With ECC, the components for those steps already exist and are consistent — but you still assemble and trigger them. The value is repeatability of the parts, not automation of the whole.
ECC vs CLAUDE.md vs a Custom Agent Setup
The fair comparison is not “one CLAUDE.md file vs a framework”. A serious custom setup can include CLAUDE.md, hooks, agents, skills, MCP servers and memory files all maintained by hand. The real question is what you maintain and what you get:
| Setup | What you maintain | What you get |
|---|---|---|
| Plain CLAUDE.md-only | Project instructions in one file | Useful context for one project; nothing automated |
| Custom harness setup (self-built) | CLAUDE.md/AGENTS.md + skills + hooks + agents + MCP + scripts, all yours | Full control; you design, debug and update everything |
| ECC | A packaged component set you select and configure | A proven baseline, shared across projects, with design choices you inherit |
If you compare ECC against a plain CLAUDE.md file, ECC wins on scope almost by definition — you are comparing one file with a whole system. The more honest comparison is against a custom harness setup you build yourself, where the tradeoff is real:
| Capability | Self-built custom setup | ECC |
|---|---|---|
| Project conventions | Yes, you write them | Yes, packaged and shared |
| Reusable task skills | Yes, if you build them | Yes, plus community/derivative skills |
| Specialized agent roles | Yes, if you define them | Yes, predefined |
| Automated checks on actions | Yes, if you write the hooks | Yes, via configured hooks |
| Persistent memory | Yes, if you build the convention | Yes, via memory conventions |
| Security scanning | Your responsibility | AgentShield included |
| Maintenance | You maintain every piece | You maintain what you selected |
The practical conclusion: a well-built personal setup can give you most of ECC’s benefits. ECC’s advantage is that the design work is already done and shared; its cost is that you inherit someone else’s design choices.
Which AI Coding Harnesses Does ECC Support?
Cross-harness support does not mean identical behavior across harnesses. This is the most important caveat in this post, so it is worth repeating: ECC’s integration depth differs by harness, and the differences change as the project iterates. Check the current repository before relying on any harness-specific behavior.
Roughly, the current landscape looks like this:
- Claude Code — native / first-class. ECC grew out of Claude Code, and its main mechanisms map to Claude Code natives: skills to Claude Code skills, hooks to Claude Code hooks, agents to Claude Code subagents, rules to CLAUDE.md-style conventions. Most ECC documentation and examples target this harness.
- OpenAI Codex — officially adapted. ECC maintains an explicit Codex-oriented path, typically mapping rules to AGENTS.md-style conventions. The mapping is not one-to-one — skills, agents and hooks do not behave identically in Codex, and feature parity should not be assumed. This is the secondary target with the most development behind it.
- OpenCode, Cursor, Gemini, Zed — partial / community / experimental. These are referenced as additional targets, but their adapters are less complete and change quickly. Treat them as partial or experimental until you verify the current state in the repository.
Why not a big capability matrix here? Because a matrix full of “depends on harness” and “where supported” cells does not help you make a decision, and any cell I fill in today could be wrong next week. The useful statement is short: Claude Code is the native environment; Codex is the main adapted environment; everything else is partial. If you are choosing between harnesses, read the repository’s current harness documentation rather than relying on a static table.
Why ECC Is Useful
Setting aside popularity, the practical advantages of this architecture are:
- Faster setup — a new project starts from a proven baseline instead of blank instructions.
- Repeatable workflow — plan, test, review and verify happen consistently when you run them, not only when you remember to ask.
- Reusable skills — once a skill is written and tested, it works across projects.
- Fewer duplicated configs — you stop maintaining the same rules in five places.
- Consistent review and planning — the same review standard applies every time because it is defined once.
- A security starting point — AgentShield gives a solo developer a check they would otherwise forget to build.
- Portability — the setup is designed to move between tools, even though behavior is not identical everywhere.
- An ecosystem — community-built and derivative skills keep extending the system.
One honest caveat: most of these benefits follow from how carefully you select and maintain components, not from installing ECC. A well-maintained personal setup can deliver many of the same results.
Where ECC Adds Complexity
There is a real counter-argument, worth reading before you install anything:
- Complexity — a multi-part system is harder to understand than one CLAUDE.md file.
- Context overhead — loading many rules, skills and memory entries consumes context window and raises token usage.
- Too many components — dozens of skills and agents can get in each other’s way.
- Overlapping rules — when multiple rules cover the same topic, behavior becomes unpredictable.
- Hook conflicts — hooks from different sources can fight over the same events.
- Maintenance cost — every layer you add is something you must update when tools change.
- Hard to know what is active — with many moving parts, it is not always clear why the agent acted a certain way.
A reasonable adoption strategy is selective: take the skills, rules and hooks you actually need, understand what is active, and skip the rest. Treating ECC as an “install everything” bundle is the most common mistake and the most common source of the complaints above.
Should You Use ECC?
Good fit — you work across multiple AI coding tools or many projects, you want consistent review and testing flows, and you are comfortable investing time to configure and maintain the system. ECC gives you a shared layer instead of per-project duplication.
Maybe — you use one tool for personal projects and your current setup is a single CLAUDE.md plus a few prompts. ECC can still help, but start by cherry-picking a small number of skills and rules instead of adopting the whole system.
Probably unnecessary — you are a beginner still learning how one agentic coding tool behaves, or you mostly write short, one-off scripts. The overhead of a full system will slow you down more than it helps. Learn the tool first, then revisit ECC later.
Quick Start
This page covers the “what” and “why”, not a full tutorial. For the minimal first steps:
- Read the current repository README at github.com/affaan-m/ECC. The recommended install method changes over time, so the README — not this page — is the source of truth.
- If you use Claude Code, follow the README’s Claude Code install path. Claude Code is the native environment, so this is the smoothest route and the one the project documents most thoroughly.
- If you use OpenAI Codex, follow the README’s Codex path and the companion guide How to Use ECC with OpenAI Codex, and expect fewer features than on Claude Code.
- Choose components, don’t install everything. Look at what the repo offers, pick a small set you will actually use, and skip the rest. You can add more later.
A dedicated How to Install ECC with Claude Code guide covers the step-by-step Claude Code setup, and How to Use ECC with OpenAI Codex covers the Codex path. This post exists to give you the concepts and the decision framework; those guides give you the execution.
FAQ
Is Everything Claude Code official Anthropic software? No. ECC is a community project created and maintained by affaan-m. It is built on top of Claude Code and other AI coding tools, but it is not affiliated with or endorsed by Anthropic.
Does ECC replace CLAUDE.md? No. CLAUDE.md is an instruction file; ECC is a component system that includes rules and memory conventions on top of what a CLAUDE.md does. Many ECC setups still use CLAUDE.md-style files — ECC packages and shares that kind of context rather than removing the need for it.
Does ECC work with OpenAI Codex? Yes, through an explicit adaptation path — but do not assume feature parity. Skills, agents and hooks do not behave identically in Codex, and support evolves quickly. Check the current repository and the Codex guide for what is actually supported today.
Does ECC work with OpenCode or Cursor? They are referenced as additional targets, but their support is more partial and faster-changing than Claude Code or Codex. Treat them as experimental until you verify the current state in the repository.
Does ECC automatically use all installed skills? No. Skills are loaded when a task matches or when you invoke them. ECC does not load every installed skill into every session, and you generally should not want it to — that would waste context and cause conflicts.
Does ECC add token or context overhead? It can. Rules, skills and memory that get loaded consume context and tokens. Selective adoption keeps overhead low; installing and loading everything is the main way people make ECC expensive.
Is AgentShield part of ECC? Yes. AgentShield is the security component that ships inside ECC. It scans agent-facing configuration and files for dangerous patterns, and where blocking occurs it works through the harness’s hook mechanism. It is not a standalone runtime sandbox.
Should I install everything in ECC? Probably not. Install what you need, understand what is active, and skip the rest. “Install everything” tends to add complexity, context cost and conflicts without a clear benefit.
ECC vs Superpowers — what is the difference? Both are community projects that extend AI coding agents with reusable capabilities. Superpowers is best known as a Claude Code skills collection, while ECC positions itself as a broader layer that also covers agents, rules, hooks, memory and security across multiple harnesses. Both projects evolve quickly, so a detailed comparison deserves its own post rather than a paragraph here.
Final Take
ECC’s real positioning is a reusable engineering layer for AI coding agents, not a prompt pack and not official Anthropic software. Its biggest value is consistency: packaged skills, rules, agents and memory let you apply the same engineering practices across projects and — to a lesser, harness-dependent degree — across tools. Its biggest tradeoff is complexity: every component you adopt is something to understand, maintain and debug, and nothing improves itself automatically.
A reasonable adoption strategy is to start small: use Claude Code as the native environment, cherry-pick a few components you will actually use, and add more only when a real need shows up. If you need one tool and a single CLAUDE.md still serves you, ECC is optional, not required.
Next steps:
- Read the current ECC repository README before installing anything.
- Follow the dedicated guide How to Install ECC with Claude Code for a step-by-step setup.
- Follow How to Use ECC with OpenAI Codex if Codex is your secondary harness.
- If you are weighing ECC against a skills collection like Superpowers, look for the dedicated comparison post.
Final Words + More Resources
My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me
Here are also the most important links from this article along with some further resources that will help you in this scope:
- 👨💻 Everything Claude Code (ECC) on GitHub - affaan-m
- 👨💻 Is Everything Claude Code Worth It? ECC vs Superpowers, Skills Packs and Custom Setups
- 👨💻 How to Install Everything Claude Code (ECC): Claude Code Setup and Best Practices
- 👨💻 How to Use ECC with OpenAI Codex: Skills, Agents, AGENTS.md and Plugin Setup
Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!
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